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| import unittest | |
| import os | |
| import pandas as pd | |
| import numpy as np | |
| # Import the function to test | |
| from auto_causal.components.dataset_analyzer import analyze_dataset | |
| # Helper to create dummy dataset files | |
| def create_dummy_csv_for_analysis(path, data_dict): | |
| df = pd.DataFrame(data_dict) | |
| df.to_csv(path, index=False) | |
| return path | |
| class TestDatasetAnalyzer(unittest.TestCase): | |
| def setUp(self): | |
| '''Set up dummy data paths and create files.''' | |
| self.test_files = [] | |
| # Basic data | |
| self.basic_data_path = "analyzer_test_basic.csv" | |
| create_dummy_csv_for_analysis(self.basic_data_path, { | |
| 'treatment': [0, 1, 0, 1, 0, 1], | |
| 'outcome': [10, 12, 11, 13, 9, 14], | |
| 'cov1': ['A', 'B', 'A', 'B', 'A', 'B'], | |
| 'numeric_cov': [1.1, 2.2, 1.3, 2.5, 1.0, 2.9] | |
| }) | |
| self.test_files.append(self.basic_data_path) | |
| # Panel data | |
| self.panel_data_path = "analyzer_test_panel.csv" | |
| create_dummy_csv_for_analysis(self.panel_data_path, { | |
| 'unit': [1, 1, 2, 2], | |
| 'year': [2000, 2001, 2000, 2001], | |
| 'treat': [0, 1, 0, 0], | |
| 'value': [5, 6, 7, 7.5] | |
| }) | |
| self.test_files.append(self.panel_data_path) | |
| # Data with potential instrument | |
| self.iv_data_path = "analyzer_test_iv.csv" | |
| create_dummy_csv_for_analysis(self.iv_data_path, { | |
| 'Z_assigned': [0, 1, 0, 1], | |
| 'D_actual': [0, 0, 0, 1], | |
| 'Y_outcome': [10, 11, 12, 15] | |
| }) | |
| self.test_files.append(self.iv_data_path) | |
| # Data with discontinuity | |
| self.rdd_data_path = "analyzer_test_rdd.csv" | |
| create_dummy_csv_for_analysis(self.rdd_data_path, { | |
| 'running_var': [-1.5, -0.5, 0.5, 1.5, -1.1, 0.8], | |
| 'outcome_rdd': [4, 5, 10, 11, 4.5, 10.5] | |
| }) | |
| self.test_files.append(self.rdd_data_path) | |
| def tearDown(self): | |
| '''Clean up dummy files.''' | |
| for f in self.test_files: | |
| if os.path.exists(f): | |
| os.remove(f) | |
| def test_analyze_basic_structure(self): | |
| '''Test the basic structure and keys of the summarized output.''' | |
| result = analyze_dataset(self.basic_data_path) | |
| self.assertIsInstance(result, dict) | |
| self.assertNotIn("error", result, f"Analysis failed: {result.get('error')}") | |
| expected_keys = [ | |
| "dataset_info", "columns", "potential_treatments", "potential_outcomes", | |
| "temporal_structure_detected", "panel_data_detected", | |
| "potential_instruments_detected", "discontinuities_detected" | |
| ] | |
| # Check old detailed keys are NOT present | |
| unexpected_keys = [ | |
| "column_types", "column_categories", "missing_values", "correlations", | |
| "discontinuities", "variable_relationships", "column_type_summary", | |
| "missing_value_summary", "discontinuity_summary", "relationship_summary" | |
| ] | |
| for key in expected_keys: | |
| self.assertIn(key, result, f"Expected key '{key}' missing.") | |
| for key in unexpected_keys: | |
| self.assertNotIn(key, result, f"Unexpected key '{key}' present.") | |
| # Check some types | |
| self.assertIsInstance(result["columns"], list) | |
| self.assertIsInstance(result["potential_treatments"], list) | |
| self.assertIsInstance(result["potential_outcomes"], list) | |
| self.assertIsInstance(result["temporal_structure_detected"], bool) | |
| self.assertIsInstance(result["panel_data_detected"], bool) | |
| self.assertIsInstance(result["potential_instruments_detected"], bool) | |
| self.assertIsInstance(result["discontinuities_detected"], bool) | |
| def test_analyze_panel_data(self): | |
| '''Test detection of panel data structure.''' | |
| result = analyze_dataset(self.panel_data_path) | |
| self.assertTrue(result["temporal_structure_detected"]) | |
| self.assertTrue(result["panel_data_detected"]) | |
| self.assertIn('year', result["columns"]) # Check columns list is correct | |
| self.assertIn('unit', result["columns"]) | |
| def test_analyze_iv_data(self): | |
| '''Test detection of potential IV.''' | |
| result = analyze_dataset(self.iv_data_path) | |
| self.assertTrue(result["potential_instruments_detected"]) | |
| def test_analyze_rdd_data(self): | |
| '''Test detection of potential discontinuity.''' | |
| # Note: Our summarized output only has a boolean flag. | |
| # The internal detection logic might be complex, but output is simple. | |
| result = analyze_dataset(self.rdd_data_path) | |
| # This depends heavily on the thresholds in detect_discontinuities | |
| # It might be False if the dummy data doesn't trigger it reliably | |
| # self.assertTrue(result["discontinuities_detected"]) | |
| # For now, just check the key exists | |
| self.assertIn("discontinuities_detected", result) | |
| def test_analyze_file_not_found(self): | |
| '''Test handling of non-existent file.''' | |
| result = analyze_dataset("non_existent_file.csv") | |
| self.assertIn("error", result) | |
| self.assertIn("not found", result["error"]) | |
| if __name__ == '__main__': | |
| unittest.main() |